Reducing Energy Consumption in Residential Buildings: The Impacts of Occupant Behaviour and Engaging Control Systems
Bibliographic record
Abstract
In Canada, buildings account for 35% of energy consumption and hold the largest opportunity for reducing energy consumption and greenhouse gas emissions.Occupant engagement poses one of the best solutions to reducing building energy consumption, with studies showing annual energy consumption can vary by up to 150% between active and passive occupants.In this thesis, an occupant-in-the-loop smart home energy system is designed and tested to explore how such systems can reduce building energy consumption through automation and occupant engagement.Simulation studies and a 125-participant survey were conducted to understand how to engage occupants to take action and their impact on home energy consumption.Insights from these studies were used to develop the smart home control system and reinforce design decisions.Testing results show 250 kWh of plug-load energy reduction and reinforce this projects conclusion that occupant-in-the-loop smart home energy systems can provide energy savings and increased occupant energy awareness/engagement. I'd like to thank my thesis supervisors Professors Liam O'Brien and Scott Bucking for their support during my research journey.Their ability to provide direction and encouragement while also allowing me to be creative and lead my own research, was of tremendous value to me.They are role models for what it means to be great mentors and supervisors.I'd also like to thank my colleague Dr. Mohamed Ouf who provided guidance and support throughout my research.This project was made possible by the team at Windmill Developments who allowed me access to data on their new Zibi development, including energy systems and building design information on specific residential buildings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".